Method and device for detecting abnormal state of excitation winding of synchronous motor

Abstract

The application discloses a kind of synchronous motor excitation winding state exception detection method and device, comprising: obtaining the ground line zero sequence current data of excitation winding, excitation voltage data and excitation current data;Store the no-load characteristic curve data of synchronous motor, and the inductance parameter and resistance parameter of excitation winding;The ground line zero sequence current data, excitation voltage data and excitation current data are preprocessed, to obtain normalized data, and excitation voltage data and excitation current data in normalized data are compared with the no-load characteristic curve data respectively, to generate corresponding comparison deviation data;According to normalized data and comparison deviation data, combine the preset state judging rule, judge whether excitation winding is in short-circuit, open-circuit or ground abnormal state, and output corresponding state determination result.The application solves the problem that the prior art detection target is single and the device is dispersed, and improves the detection efficiency.

Description

Technical Field

[0001] This invention relates to the field of electronic technology, and specifically to a method and apparatus for detecting abnormal conditions in the excitation winding of a synchronous motor. Background Technology

[0002] Synchronous motors are core power equipment in power systems and industrial production, and their operational stability directly affects the safe and reliable operation of the entire system. The excitation winding, as a key component in the synchronous motor's electromechanical energy conversion, has a decisive impact on the motor's performance. During long-term operation, synchronous motors are susceptible to abnormal faults such as inter-turn short circuits, open circuits, and grounding due to factors such as insulation aging, mechanical vibration, electromagnetic shock, and environmental factors.

[0003] Currently, detection technologies for abnormal states of synchronous motor excitation windings are quite fragmented, and most detection methods can only diagnose a single fault type, for example:

[0004] Grounding fault detection: Insulation resistance is usually measured by DC or AC injection method, or the grounding wire current is directly monitored. The methods are isolated and difficult to correlate with other faults.

[0005] Inter-turn short circuit detection: Traditional methods rely on offline measurement of winding DC resistance and reactance or impulse voltage tests, which cannot achieve online real-time monitoring. Some online methods analyze indirect signals such as air gap magnetic field, stator current harmonics, or rotor vibration, but these signals are easily affected by factors such as load and power grid fluctuations, and are not sensitive to minor short circuits.

[0006] Open circuit fault detection: It is mainly determined by monitoring whether the excitation current is zero or the excitation voltage rises abnormally. However, it is easily confused with faults in the excitation system (such as power components and trigger circuits) and it is difficult to accurately locate the open circuit of the winding itself.

[0007] The main problems with existing technologies are as follows: First, there is a lack of an integrated online detection method that can comprehensively utilize multiple key electrical signals to diagnose and differentiate three typical excitation winding faults: short circuit, grounding, and open circuit. Second, the diagnostic logic relies heavily on simple threshold comparisons and fails to fully utilize the inherent physical characteristics of the motor (such as the no-load characteristic curve) as a benchmark, resulting in insufficient fault feature extraction and a single judgment criterion. Under complex operating conditions (such as sudden load changes and power grid disturbances), it is easy to produce misjudgments or omissions. Third, the diagnostic process lacks intelligence and fails to dynamically adjust the criteria based on historical data and operating status, resulting in limited adaptability and early warning capabilities. Summary of the Invention

[0008] In order to overcome the technical shortcomings of the existing technology that only has a single detection type, the present invention provides a method and device for detecting abnormal conditions of the excitation winding of a synchronous motor.

[0009] To solve the above problems, the present invention is implemented according to the following technical solution:

[0010] In a first aspect, the present invention provides a method for detecting abnormal states of the excitation winding of a synchronous motor, comprising the following steps: acquiring ground wire zero-sequence current data, excitation voltage data, and excitation current data of the excitation winding; storing no-load characteristic curve data of the synchronous motor, as well as inductance and resistance parameters of the excitation winding; preprocessing the ground wire zero-sequence current data, excitation voltage data, and excitation current data to obtain normalized data, and comparing the excitation voltage data and excitation current data in the normalized data with the no-load characteristic curve data respectively to generate corresponding comparison deviation data; determining whether the excitation winding is in a short-circuit, open-circuit, or grounding abnormal state based on the normalized data and the comparison deviation data, combined with preset state determination rules, and outputting the corresponding state determination result.

[0011] In conjunction with the first aspect, the present invention provides a first specific implementation of the first aspect. Specifically, the preset state determination rule includes the following logical determination process: Grounding state determination logic: Under the premise that the synchronous motor is determined to be in a stable operating state, if the grounding wire zero-sequence current data continuously exceeds the grounding wire zero-sequence current threshold, and the insulation resistance data of the excitation winding is lower than the preset insulation resistance threshold, then the excitation winding is determined to be in a grounding abnormal state; Open circuit state determination logic: If the excitation current data shows a downward trend and the excitation voltage data shows an upward trend, and the rotor time constant change of the excitation winding exceeds the preset time constant change threshold, then the excitation winding is determined to be in an open circuit abnormal state; Short circuit state determination logic: If the excitation current data shows an upward trend, the excitation voltage data shows a downward trend, and the comparison deviation data exceeds the comparison deviation threshold, and the rotor time constant change exceeds the preset time constant change threshold, then the excitation winding is determined to be in a short circuit abnormal state.

[0012] In conjunction with the first aspect, the present invention provides a second specific implementation of the first aspect. Specifically, storing the no-load characteristic curve data of the synchronous motor, as well as the inductance and resistance parameters of the excitation winding, includes the following steps: receiving and parsing a structured data file from a motor factory report or a remote database, wherein the structured data file at least contains a no-load characteristic curve expressed in the form of a voltage-current data pair sequence, and the rated inductance and rated resistance values ​​of the excitation winding; performing normalized interpolation processing on the voltage-current data pair sequence of the no-load characteristic curve to generate a standardized no-load characteristic curve lookup table, and storing the standardized no-load characteristic curve lookup table in association with the rated inductance and rated resistance values ​​as a characteristic parameter set of the synchronous motor; and storing estimated values ​​of the inductance and resistance parameters in the characteristic parameter set according to the historical operating data or online identification results of the synchronous motor.

[0013] In conjunction with the first aspect, the present invention provides a third specific implementation of the first aspect. Specifically, the grounding wire zero-sequence current data, excitation voltage data, and excitation current data are preprocessed to obtain normalized data. The excitation voltage data and excitation current data in the normalized data are then compared with the no-load characteristic curve data to generate corresponding comparison deviation data. This specifically includes the following steps: digitally filtering the acquired grounding wire zero-sequence current data, excitation voltage data, and excitation current data; and normalizing the excitation voltage data and excitation current data, wherein the normalization process uses the following formula to map the actual data to a preset numerical range: ;in, , These are the actual excitation voltage and excitation current values, respectively. , These are the preset upper and lower limits of the excitation voltage, respectively. , These are the preset upper and lower limits of the excitation current, respectively; the normalized excitation voltage data and excitation current data are compared with the no-load characteristic curve data to calculate the comparison deviation data.

[0014] In conjunction with the first aspect, the present invention provides a fourth specific implementation of the first aspect. Specifically, the step of comparing the normalized excitation voltage data and excitation current data with the no-load characteristic curve data to calculate the comparison deviation data includes the following steps: based on the normalized excitation current value... The corresponding expected value of the standard excitation voltage is obtained by interpolation lookup in the standardized no-load characteristic curve lookup table. ; Calculate the measured normalized excitation voltage value Compared with the expected value of the standard excitation voltage absolute deviation and relative deviation The calculation formulas are as follows: The absolute deviation With the relative deviation Together they serve as the comparison deviation data.

[0015] In conjunction with the first aspect, the present invention provides a fifth specific implementation of the first aspect. Specifically, the acquisition of the zero-sequence current data, excitation voltage data, and excitation current data of the excitation winding ground wire includes the following steps: establishing a data communication connection with an external monitoring system, wherein the external monitoring system includes at least a zero-sequence current transformer located on the excitation winding ground wire, a voltage sensor located at both ends of the excitation winding, and a current sensor located in the excitation circuit; synchronously receiving the raw data stream of the ground wire zero-sequence current data, excitation voltage data, and excitation current data uploaded by the external monitoring system through the data communication connection according to a preset sampling frequency and data format; performing timestamp alignment and data packet integrity verification on the raw data stream, and removing abnormal timestamp data and incomplete data packets to form time-series data.

[0016] In conjunction with the first aspect, the present invention provides a sixth specific implementation of the first aspect, specifically, generating a diagnostic report containing anomaly codes, suggested handling measures, and estimated risk levels based on the type and severity level of the state determination result; packaging and storing the diagnostic report, the key data sequence that triggered the determination, and the corresponding comparison deviation data in a historical fault case library; and automatically generating and sending a warning instruction for load reduction operation or shutdown inspection to the synchronous motor monitoring system if the determination result is a short circuit or grounding anomaly.

[0017] Secondly, the present invention also provides a detection device for abnormal excitation winding state of a synchronous motor, comprising: a data acquisition module for acquiring ground wire zero-sequence current data, excitation voltage data, and excitation current data of the excitation winding; a data storage module for storing no-load characteristic curve data of the synchronous motor, as well as inductance and resistance parameters of the excitation winding; a data processing module for preprocessing the ground wire zero-sequence current data, excitation voltage data, and excitation current data to obtain normalized data, and comparing the excitation voltage data and excitation current data in the normalized data with the no-load characteristic curve data respectively to generate corresponding comparison deviation data; and a state determination module for determining whether the excitation winding is in a short-circuit, open-circuit, or grounding abnormal state based on the normalized data and the comparison deviation data, combined with preset state determination rules, and outputting the corresponding state determination result.

[0018] Compared with the prior art, the beneficial effects of the present invention are:

[0019] This application achieves integrated online detection and differentiation of three typical abnormal states of the excitation winding: short circuit, grounding, and open circuit. It solves the problems of single detection target and scattered devices in existing technologies, improving detection efficiency and equipment utilization. By fully utilizing the inherent no-load characteristic curve of the motor as a benchmark, combined with real-time acquired multi-parameter data and their changing trends, and through comprehensive judgment using composite logic rules, it significantly reduces misjudgments and missed judgments caused by single signal fluctuations or external interference, improving the accuracy and reliability of fault identification. Attached Figure Description

[0020] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings, wherein:

[0021] Figure 1 This is a flowchart of a method for detecting abnormal conditions in the excitation winding of a synchronous motor according to the present invention.

[0022] Figure 2 This is a flowchart of a detection device for abnormal excitation winding status of a synchronous motor according to the present invention.

[0023] Figure 3 This is a block diagram of an electronic device used to implement embodiments of the present invention;

[0024] In the picture:

[0025] 100 - Electronic device, 101 - Computing unit, 102 - ROM, 103 - RAM, 104 - Bus, 105 - I / O interface, 106 - Input unit, 107 - Output unit, 108 - Storage unit, 109 - Communication unit. Detailed Implementation

[0026] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0027] like Figures 1-3 As shown, this invention provides a method and apparatus for detecting abnormal states of the excitation winding of a synchronous motor.

[0028] Example 1

[0029] like Figure 1As shown, a method for detecting abnormal excitation winding conditions of a synchronous motor includes the following steps: acquiring ground wire zero-sequence current data, excitation voltage data, and excitation current data of the excitation winding; storing the no-load characteristic curve data of the synchronous motor, as well as the inductance and resistance parameters of the excitation winding; preprocessing the ground wire zero-sequence current data, excitation voltage data, and excitation current data to obtain normalized data, and comparing the excitation voltage data and excitation current data in the normalized data with the no-load characteristic curve data to generate corresponding comparison deviation data; based on the normalized data and the comparison deviation data, combined with preset state judgment rules, determining whether the excitation winding is in a short-circuit, open-circuit, or grounding abnormal state, and outputting the corresponding state judgment result.

[0030] Specifically, this application achieves integrated online detection and differentiation of three typical abnormal states of the excitation winding: short circuit, grounding, and open circuit. This solves the problems of single detection target and dispersed devices in existing technologies, improving detection efficiency and equipment utilization. It fully utilizes the inherent no-load characteristic curve of the motor as a benchmark, combined with real-time acquired multi-parameter data and their changing trends, and performs comprehensive judgment through composite logic rules. This significantly reduces misjudgments and missed judgments caused by single signal fluctuations or external interference, improving the accuracy and reliability of fault identification.

[0031] In a preferred embodiment, S100: acquire the zero-sequence current data of the grounding wire of the excitation winding, the excitation voltage data, and the excitation current data.

[0032] Specifically, the grounding wire zero-sequence current data is the zero-sequence current signal monitored or collected in real time from the grounding wire of the excitation winding. This data can reflect whether there is an abnormality in the insulation to ground or a grounding fault in the excitation circuit; the excitation voltage data is the DC voltage signal applied to both ends of the excitation winding; and the excitation current data is the DC current signal flowing through the excitation winding.

[0033] In a preferred embodiment, the acquisition of the grounding wire zero-sequence current data, excitation voltage data, and excitation current data of the excitation winding specifically includes the following steps:

[0034] Establish a data communication connection with an external monitoring system, which includes at least a zero-sequence current transformer located on the grounding wire of the excitation winding, a voltage sensor located at both ends of the excitation winding, and a current sensor located in the excitation circuit.

[0035] Specifically, a data communication connection is established between this system and an external distributed monitoring system. The external monitoring system is typically installed at key measurement points in the synchronous motor excitation circuit, and its core sensing unit includes at least:

[0036] Zero-sequence current transformer is installed on the grounding line of the excitation winding and is used to detect leakage current to ground.

[0037] A voltage sensor is connected across the two ends of the excitation winding to measure the excitation voltage;

[0038] A current sensor, connected in series in the excitation circuit, is used to measure the excitation current.

[0039] According to the preset sampling frequency and data format, the raw data streams of the grounding wire zero-sequence current data, excitation voltage data and excitation current data uploaded by the external monitoring system are synchronously received through the data communication connection.

[0040] Specifically, the system sends synchronous acquisition commands to the external monitoring system via an established communication link (such as industrial Ethernet, fieldbus, etc.) according to a preset sampling frequency (e.g., set to 1kHz to 10kHz based on the transient response characteristics of the excitation system) and a unified data format protocol. Subsequently, the system synchronously receives raw data streams uploaded from three independent channels in a streaming manner.

[0041] Original waveform data stream of zero-sequence current in grounding wire;

[0042] Raw sampled data stream of excitation voltage;

[0043] The original sampled data stream of the excitation current is processed. The original data stream is then timestamped and its integrity verified. Abnormal timestamps and incomplete data packets are removed to create multi-channel time-synchronized timing data.

[0044] Specifically, the received multi-channel raw data stream is preprocessed:

[0045] Timestamp alignment: An absolute timestamp is appended to each sampled data point. By comparing and correcting the timestamps of the data from each channel, all data are mapped to the same time reference axis, ensuring strict synchronization between data and eliminating phase errors caused by transmission delays or asynchronous acquisition.

[0046] Packet integrity verification: Inspect each received data packet. Verify its transmission integrity using a checksum embedded in the packet (such as CRC check). Discard incomplete data packets that fail verification due to communication interference, are missing data bits, or are out of order.

[0047] Preliminary removal of abnormal data: Based on time alignment, the data is judged for reasonableness according to preset physical ranges (such as the normal operating range of excitation current and voltage). Abnormal data points that clearly exceed the range, have transient spikes (non-fault characteristics), or have logical errors in timestamps (such as reverse time jumps) are removed.

[0048] In a preferred embodiment, S200: stores the no-load characteristic curve data of the synchronous motor, as well as the inductance and resistance parameters of the excitation winding.

[0049] Specifically, the no-load characteristic curve data records the excitation current of the synchronous motor at rated speed and with the armature winding open (no-load) condition. armature terminal voltage The nonlinear relationship between the induced electromotive force (EMF) and the magnetization characteristic of the motor is shown in the curve. This curve represents the fundamental magnetization characteristic of the motor and is typically stored as a set of discrete data points, such as a series of excitation current values ​​varying from small to large and their corresponding measured or per-unit armature voltage values. These data provide a reference magnetization characteristic for assessing the degree of deviation between the current operating point and the rated magnetic circuit state.

[0050] Inductance parameters of the excitation winding This mainly refers to the self-inductance of the excitation winding, a parameter related to the degree of magnetic circuit saturation of the motor. In actual storage, it can be a curve showing the inductance value as a function of the excitation current, or the equivalent linear inductance value at a specific operating point (such as the rated no-load point).

[0051] Resistance parameters of excitation winding This refers to the DC resistance of the excitation winding at a reference temperature (typically 75°C). This parameter is used to calculate the ohmic voltage drop in the excitation circuit and the heat loss of the winding.

[0052] In a preferred embodiment, storing the no-load characteristic curve data of the synchronous motor, as well as the inductance and resistance parameters of the excitation winding, specifically includes the following steps:

[0053] Receive and parse structured data files from motor factory reports or remote databases. The structured data files contain at least no-load characteristic curves expressed in the form of voltage-current data pairs, as well as the rated inductance and rated resistance values ​​of the excitation winding.

[0054] Specifically, a structured data file describing the static characteristics of the target synchronous motor is received and parsed through a pre-defined data interface. This file typically originates from the motor's factory type test report or a remote database maintained by the equipment management system. The file content must include at least:

[0055] No-load characteristic curve data: in discrete "armature voltage ( ) - Excitation current ( The data is given in the form of a sequence, which fully describes the saturation magnetization process from the remanent magnetization voltage to above the rated voltage (e.g., 1.3 times the rated voltage).

[0056] Nominal parameters of the excitation winding: Provides the rated inductance and rated resistance values ​​of the excitation winding measured under standard reference conditions.

[0057] The voltage-current data sequence of the no-load characteristic curve is normalized and interpolated to generate a standardized no-load characteristic curve lookup table. The standardized no-load characteristic curve lookup table is then associated with the rated inductance value and rated resistance value and stored as a characteristic parameter set of the synchronous motor.

[0058] Specifically, the original discrete voltage-current data sequence is normalized, transforming it into a per-unit system based on rated voltage and rated excitation current to eliminate the influence of specific numerical dimensions. Based on the normalized data point sequence, algorithms such as spline interpolation or high-order polynomial interpolation are used to generate a standardized no-load characteristic curve. Based on this curve, the corresponding armature voltage value is calculated and stored at preset resolution excitation current intervals (e.g., with a step size of 0.1% or 0.5% per unit), forming a standardized no-load characteristic curve lookup table. The standardized no-load characteristic curve lookup table, rated inductance value, and rated resistance value are associated and encapsulated into a characteristic parameter set. This characteristic parameter set is identified by a unique motor model or device ID and stored in the system's local or cloud-based parameter database.

[0059] Based on the historical operating data or online identification results of the synchronous motor, the real-time estimated values ​​of the inductance and resistance parameters and their effective time range are dynamically updated or additionally stored in the characteristic parameter set.

[0060] Specifically, based on acquired long-term historical operating data (such as voltage and current data under specific steady-state conditions), the system can periodically recalculate the equivalent inductance and resistance parameters of the excitation winding. These calculated real-time estimates, their corresponding confidence levels, and valid time ranges (e.g., "estimates at an equivalent temperature of 75°C between October 2023 and March 2024") are updated to the motor's characteristic parameter set as supplementary data records or historical versions.

[0061] In a preferred embodiment, the grounding wire zero-sequence current data, excitation voltage data, and excitation current data are preprocessed to obtain normalized data. The excitation voltage data and excitation current data in the normalized data are then compared with the no-load characteristic curve data to generate corresponding comparison deviation data.

[0062] Specifically, the multi-channel time-series data is normalized, including:

[0063] Zero-sequence current data: The DC component of the data sequence is removed (e.g., its moving average is subtracted) to highlight its AC fluctuation characteristics, resulting in a normalized zero-sequence current that only reflects the change in unbalanced current to ground.

[0064] Excitation voltage and current data: Both are converted to per-unit values. The excitation voltage is based on the rated excitation voltage, and the excitation current is based on the excitation current corresponding to the rated no-load voltage. The purpose is to map the measured values ​​to the same per-unit coordinate system as the stored no-load characteristic curve. The normalized real-time excitation voltage... With excitation current The data is compared online with the standardized no-load characteristic curve lookup table stored in step S200.

[0065] Comparison method: Compare the measured data at each synchronization time point. In the standardized no-load characteristic curve lookup table, use interpolation methods (such as linear interpolation) to find the curve that matches the current measured excitation current. The corresponding theoretical unloaded armature voltage value .

[0066] Calculate the measured excitation voltage Compared with the theoretical no-load voltage found Deviation between ( This deviation value sequence This is the key comparison result. Its physical meaning lies in the difference between the actual applied excitation voltage and the theoretically required excitation voltage to establish the rated no-load voltage under a given excitation current. This deviation value... Theoretically, it mainly reflects the additional voltage drop on the excitation circuit resistance (including brush contact resistance, etc.). Abnormal changes (such as continuous increase) are important indicators of poor contact, aging joints, or partial short circuit faults in the excitation winding circuit.

[0067] In a preferred embodiment, the preprocessing of the grounding wire zero-sequence current data, excitation voltage data, and excitation current data to obtain normalized data, and the comparison of the excitation voltage data and excitation current data in the normalized data with the no-load characteristic curve data to generate corresponding comparison deviation data, specifically includes the following steps:

[0068] The acquired grounding wire zero-sequence current data, excitation voltage data, and excitation current data are digitally filtered.

[0069] Specifically, for grounding wire zero-sequence current data: a bandpass filter is used to retain the frequency band that can reflect the characteristics of typical grounding faults (such as 0.1Hz to several hundred Hz).

[0070] For excitation voltage and excitation current data: a low-pass filter is used to filter out high-frequency noise that is much higher than the excitation regulation bandwidth, ensuring the accuracy of steady-state and slowly varying components. Its cutoff frequency is set according to the dynamic response characteristics of the excitation system.

[0071] The excitation voltage data and excitation current data are normalized, wherein the normalization process maps the actual data to a preset numerical range using the following formula:

[0072] ;

[0073] in, , These are the actual excitation voltage and excitation current values, respectively. , These are the preset upper and lower limits of the excitation voltage, respectively. , These are the preset upper and lower limits of the excitation current, respectively;

[0074] Specifically, the filtered excitation voltage and excitation current data are normalized to map their actual engineering values ​​to a preset numerical range (e.g., ...). or This eliminates dimensional differences and facilitates standardized comparison and calculation.

[0075] The normalization process is calculated using the following formula:

[0076] For excitation voltage: ;

[0077] For the excitation current: ;

[0078] in, , These are the instantaneous values ​​of the actual excitation voltage and excitation current after filtering, respectively.

[0079] and These are the preset lower and upper limits of the excitation voltage, determined based on the motor's rated operating range or historical data statistics.

[0080] and These are the preset lower and upper limits of the excitation current mapping, determined based on the range of the no-load characteristic curve.

[0081] and These are the normalized excitation voltage and excitation current values, respectively, and their values ​​fall within a preset range.

[0082] The normalized excitation voltage data and excitation current data are compared with the no-load characteristic curve data to calculate the comparison deviation data.

[0083] Specifically, the normalized excitation voltage data With excitation current data The data is compared point-by-point in real time with the standardized no-load characteristic curve data processed by the normalized benchmark to generate comparison deviation data that characterizes the deviation of the operating state from the benchmark characteristics.

[0084] Comparison method: For each sampling time, normalized data pairs ( , The interpolation algorithm (such as linear interpolation or spline interpolation) is used to find the current value in the standard no-load characteristic curve data. The corresponding theoretical normalized no-load voltage value .

[0085] Calculate the measured normalized excitation voltage With theoretical no-load voltage The difference between them is used as the comparison bias data at that moment. :

[0086] The deviation sequence Physically, it reflects the degree of deviation of the actual excitation voltage from the ideal no-load characteristic curve under the same excitation current. This deviation is mainly caused by the voltage drop of the additional resistance in the excitation circuit (such as the contact resistance of the brush and the resistance of the connection point). Its increasing trend or drastic fluctuation is an important indicator of potential faults such as abnormal connection of the excitation circuit, winding deterioration, or the presence of inter-turn short circuits.

[0087] In a preferred embodiment, the step of comparing the normalized excitation voltage data and excitation current data with the no-load characteristic curve data to calculate the comparison deviation data specifically includes the following steps:

[0088] Based on the normalized excitation current value The corresponding expected value of the standard excitation voltage is obtained by interpolation lookup in the standardized no-load characteristic curve lookup table. ;

[0089] Specifically, for the normalized excitation current value acquired at each sampling moment... Using it as a query index, a matching query is performed in the standardized no-load characteristic curve lookup table.

[0090] Interpolation lookup operation: Since the standardized no-load characteristic curve lookup table is stored discretely, the system uses linear interpolation or an interpolation algorithm (such as cubic spline interpolation) based on... In the standardized no-load characteristic curve lookup table, two adjacent nodes ( , )and( , Interpolation calculations are performed between the two to obtain the value corresponding to the real-time excitation current. The corresponding standard excitation voltage expected value This value represents the theoretical excitation voltage that should be applied when the motor is in an ideal no-load state under the current excitation level.

[0091] Calculate the measured normalized excitation voltage value Compared with the expected value of the standard excitation voltage absolute deviation and relative deviation The calculation formulas are as follows: ;

[0092] Specifically, to obtain the standard expected value Then, it was compared with the normalized excitation voltage value measured at the same sampling time. Comparing the two, the deviations were calculated from both the absolute quantity and the relative proportion dimensions:

[0093] Absolute deviation calculation: reflects the direct numerical difference between the expected value and the measured value.

[0094] in, This is the absolute deviation value. This value visually represents the magnitude of the deviation. A sustained positive deviation may indicate the presence of additional resistance in the excitation circuit (such as poor contact), while a negative deviation should raise suspicion of measurement abnormalities or other problems.

[0095] Relative deviation calculation: Reflects the ratio of the absolute deviation to the standard expected value, and is used to assess the severity of the deviation.

[0096] ;

[0097] in, This is the relative deviation (expressed as a percentage). This indicator eliminates the influence of different base values ​​of standard voltage at different operating points (different excitation currents), making the deviations under different operating conditions comparable and facilitating the setting of a unified abnormal threshold.

[0098] The absolute deviation With the relative deviation Together they serve as the comparison deviation data.

[0099] The calculated absolute deviation and relative deviation To form a comprehensive comparison bias data pair This serves as the output at that sampling moment. The system continuously generates a time series sequence of this bias data pair.

[0100] absolute deviation Used to determine the instantaneous amplitude and trend of deviation, especially in the expected value. When it is relatively small.

[0101] relative deviation This is used to assess the severity of deviations and to make cross-operational comparisons, providing a more scientific basis for setting early warning thresholds.

[0102] In a preferred embodiment, based on the normalized data and the comparison deviation data, combined with the preset state determination rules, it is determined whether the excitation winding is in a short circuit, open circuit or grounding abnormal state, and the corresponding state determination result is output.

[0103] Specifically, abnormal states of the excitation winding mainly include short circuit, open circuit, or grounding, and the specific abnormality determination is as follows:

[0104] 1. Determination of abnormal grounding conditions

[0105] The determination of grounding status is mainly based on the analysis of standardized grounding wire zero-sequence current data, and the classification is achieved by monitoring its effective value (or peak value) and waveform characteristics:

[0106] If the zero-sequence current value continuously exceeds the lower first preset threshold (alarm threshold) but does not reach the higher second preset threshold (danger threshold), and waveform analysis detects characteristic harmonics related to the rotor rotation frequency, it is determined to be "slight insulation deterioration or unstable grounding", which is a warning state.

[0107] If the zero-sequence current value increases sharply and exceeds the second preset danger threshold, or exhibits stable power frequency (or harmonic frequency) characteristics with significant amplitude, it is determined that a "deterministic grounding fault has occurred," which is an alarm or fault state.

[0108] If the zero-sequence current value is consistently below the extremely low noise threshold, it is determined that the grounding status is "normal".

[0109] 2. Determination of inter-turn short circuit abnormality

[0110] Determining the inter-turn short circuit condition requires comprehensive analysis by comparing deviation data with excitation current and voltage data. An inter-turn short circuit will increase the excitation current required to establish the same air gap flux (corresponding to a certain voltage), or decrease the voltage that can be generated under the same excitation current.

[0111] If the absolute deviation in the comparison deviation data The value remains significantly negative, and the relative deviation is... If the negative amplitude exceeds its preset threshold, it indicates that the actual required voltage is lower than the expected value of the standard no-load characteristic curve, which serves as the main evidence of an inter-turn short circuit.

[0112] If the excitation current is detected when the unit output (active and reactive power) does not change significantly. There is a trend of increasing, while the excitation voltage The abnormal change pattern, which shows no year-on-year increase and even a decrease, can serve as supplementary evidence of inter-turn short circuits.

[0113] When the above main criteria are met and the auxiliary criteria are supported, the system determines that "an inter-turn short-circuit fault exists." The system can assess the relative size or severity level of the short-circuit fault based on the severity of the deviation.

[0114] 3. Determination of open circuit or high-resistance contact abnormalities

[0115] The determination of open circuit or high-resistance contact abnormalities is mainly based on comparing deviation data with excitation voltage and current data. An abnormal increase in circuit resistance (poor contact) or an open circuit will result in a higher terminal voltage drop under the same excitation current, or the current will not be able to flow normally.

[0116] If the absolute deviation in the comparison deviation data The value remains significantly positive, and the relative deviation is... If the positive amplitude exceeds its preset threshold, it indicates that there is an additional abnormal circuit resistance voltage drop, which can be judged as "excitation circuit contact resistance too high (warning)".

[0117] If the excitation voltage Under conditions of maintaining the command or only minor fluctuations, the excitation current If the value suddenly drops to near zero or fluctuates drastically, it may indicate that the circuit is completely open or intermittently disconnected. In this case, it is necessary to combine the status information of circuit breakers, fuses, and other switch quantities for final confirmation and determine it as "excitation circuit open circuit fault (alarm)".

[0118] The system performs parallel real-time analysis on the three abnormal states mentioned above, and finally synthesizes and outputs a structured state determination result, which typically includes the following information:

[0119] Overall status rating: such as "normal", "warning", "alarm" or "fault".

[0120] Specific anomaly type and confidence level: such as "suspected inter-turn short circuit, confidence level 85%".

[0121] Key indicator data: Key data that triggers the judgment (such as the zero-sequence current value exceeding the standard, the comparison deviation value) and its corresponding timestamp.

[0122] Recommended maintenance measures based on the status level, such as "enhanced monitoring", "planned shutdown for inspection", and "emergency shutdown".

[0123] In a preferred embodiment, the preset state determination rule includes the following logical determination process:

[0124] Grounding status determination logic: Under the premise that the synchronous motor is determined to be in a stable operating state, if the grounding wire zero-sequence current data continuously exceeds the grounding wire zero-sequence current threshold, and the insulation resistance data of the excitation winding is lower than the preset insulation resistance threshold, then the excitation winding is determined to be in an abnormal grounding state.

[0125] Specifically, the first step is to determine whether the synchronous motor is in a "stable operating state." This state is confirmed by analyzing the fluctuation rate of the generator's active power, reactive power, speed, and terminal voltage within a preset time window. Grounding detection can only be initiated when the unit is determined to be operating stably, in order to eliminate zero-sequence current interference caused by drastic changes in operating conditions.

[0126] A ground fault determination is triggered if both of the following conditions are met simultaneously:

[0127] Condition 1 (Current Criterion): Calculate the effective value or moving average of the normalized grounding wire zero-sequence current data. If this value continuously exceeds the preset grounding wire zero-sequence current threshold (Th_Gnd_Current) for a preset duration (e.g., more than 5 minutes).

[0128] Condition 2 (Resistance Criterion): Obtain (or estimate / monitor online) the insulation resistance data of the excitation winding to ground, and if its value is lower than the preset insulation resistance safety threshold (Th_Ins_Res).

[0129] If the above prerequisites are met and both core criteria are satisfied simultaneously, then the excitation winding is determined to be in an abnormal grounding state, and a corresponding alarm is generated.

[0130] Open circuit state determination logic: If the excitation current data shows a downward trend and the excitation voltage data shows an upward trend, and the rotor time constant of the excitation winding changes more than the preset time constant change threshold, then the excitation winding is determined to be in an open circuit abnormal state.

[0131] Specifically, criterion one (electrical parameter trend): within an analysis time window, the excitation current data shows a statistically significant downward trend, while the excitation voltage data shows a statistically significant upward trend. This trend can be quantified by calculating the slope of linear regression or by difference comparison.

[0132] Criterion 2 (Model Parameter Change): Based on real-time data online identification, if the change in the excitation winding-rotor time constant (or the equivalent time constant of the excitation circuit) exceeds the preset time constant change threshold (Th_Open_Tau), an open circuit or high-resistance fault will cause the circuit time constant to decrease.

[0133] If the trends of current decrease and voltage increase in "Criterion 1" are met simultaneously, and the change in time constant in "Criterion 2" exceeds the threshold, then it is determined that "the excitation winding is in an open circuit (or high resistance contact) abnormal state".

[0134] Short circuit condition determination logic: If the excitation current data shows an upward trend, the excitation voltage data shows a downward trend, and the comparison deviation data exceeds the comparison deviation threshold, and the rotor time constant change exceeds the preset time constant change threshold, then the excitation winding is determined to be in a short circuit abnormal state.

[0135] Specifically, the following three conditions must be met simultaneously to improve the specificity of the judgment and avoid false alarms:

[0136] Criterion 1 (Reverse trend of electrical parameters): Within a certain analysis time window, the excitation current data shows a statistically significant upward trend, while the excitation voltage data shows a statistically significant downward trend.

[0137] Criterion 2 (Abnormal Characteristic Curve Comparison): The absolute value of the comparison deviation data (especially the relative deviation ΔV_rel) continuously exceeds the preset comparison deviation threshold (Th_Short_Delta), and the sign indicates a negative deviation (the measured voltage is lower than the expected characteristic curve).

[0138] Criterion 3 (Dynamic Parameter Change): The change in rotor time constant obtained from online identification based on real-time data exceeds the preset time constant change threshold (Th_Short_Tau). Inter-turn short circuits typically lead to a decrease in equivalent inductance, thereby reducing the time constant.

[0139] The excitation winding is determined to be in an abnormal state of inter-turn short circuit if and only if all three criteria are met.

[0140] The system performs logical judgments in the order of ground fault determination → open circuit determination → short circuit determination. Once a certain type of fault is determined to be true, subsequent determinations for other types can be paused or carefully evaluated, as some severe faults (such as severe ground faults) may affect the measurement of other parameters. All determination results are accompanied by a snapshot of the main parameters at the time of triggering and a confidence assessment for operators to review.

[0141] In a preferred embodiment, the method further includes the following step: generating a diagnostic report containing an anomaly code, suggested handling measures, and estimated risk level based on the type and severity level of the status determination result;

[0142] Specifically, a structured diagnostic report is automatically generated based on the type and severity level of the status assessment result. The diagnostic report uses a standardized anomaly code system (e.g., "F_EFD_01" represents "minor grounding warning of the excitation winding") to uniquely identify the fault type and level. Based on the fault type and level, the report content outputs specific and actionable recommended measures. For example, for a "minor grounding warning," the recommended measures might be "strengthen online monitoring and arrange for an upcoming insulation inspection"; for an "inter-turn short circuit alarm," the recommended measure is "plan a shutdown to conduct winding resistance and impedance tests." Simultaneously, the diagnostic report comprehensively considers the nature of the fault, the current unit load, and its importance in the power grid, assessing and labeling the potential risk level of this abnormal event (e.g., "low risk," "medium risk," "high risk") to guide the prioritization of operation and maintenance decisions. The diagnostic report is promptly communicated to operation and maintenance personnel through various methods, including visual interface pop-ups, push notifications to mobile devices, and the generation of standard format files (e.g., PDF, JSON).

[0143] The diagnostic report, the key data sequence that triggered the judgment, and the corresponding comparison deviation data are packaged and stored in the historical fault case library;

[0144] Specifically, to support fault tracing, model optimization, and knowledge accumulation, the system performs data archiving:

[0145] The key data sequences triggered by this judgment (including but not limited to: raw / normalized zero-sequence current, excitation voltage, and excitation current data within the judgment window), the corresponding comparison deviation data time series, and the diagnostic report are time-aligned and logically correlated. The assembled data package is then packaged and stored in the historical fault case library, indexed by the timestamp and exception code of this event. This case library supports multi-dimensional queries and analyses based on time, fault type, severity level, and other dimensions.

[0146] If the determination result is a short circuit or grounding abnormality, an early warning command for reduced load operation or shutdown inspection will be automatically generated and sent to the synchronous motor monitoring system.

[0147] Specifically, if the status determination result is "short circuit abnormal state" or "grounding abnormal state" with the "serious alarm" or "fault" level (such as extremely low insulation resistance or clear short circuit signs), the system will automatically trigger the control command generation process.

[0148] The system automatically generates specific control recommendations. Depending on the severity of the fault, the recommendation might be "Recommend operating at reduced load to X% of rated power" or "Recommend immediately performing a shutdown inspection." This recommendation is automatically sent to the upstream synchronous motor monitoring system (DCS / ECS) or unit protection system via a preset safety communication protocol (such as IEC 61850 MMS, OPC UA, etc.).

[0149] The system records the sending status of commands and can receive command confirmations or execution feedback from the monitoring system, forming a partial closed-loop management system. Ultimately, whether to execute a shutdown or load reduction operation is still determined by the operators based on procedures and overall operating conditions.

[0150] Example 2

[0151] like Figure 2 As shown, a detection device for abnormal excitation winding state of a synchronous motor includes: a data acquisition module for acquiring ground wire zero-sequence current data, excitation voltage data, and excitation current data of the excitation winding; a data storage module for storing no-load characteristic curve data of the synchronous motor, as well as inductance and resistance parameters of the excitation winding; a data processing module for preprocessing the ground wire zero-sequence current data, excitation voltage data, and excitation current data to obtain normalized data, and comparing the excitation voltage data and excitation current data in the normalized data with the no-load characteristic curve data to generate corresponding comparison deviation data; and a state determination module for determining whether the excitation winding is in a short-circuit, open-circuit, or grounding abnormal state based on the normalized data and the comparison deviation data, combined with preset state determination rules, and outputting the corresponding state determination result.

[0152] Specifically, the data acquisition module is used to collect key electrical parameters of the synchronous motor excitation system, which includes a zero-sequence current transformer, a voltage sensor, a shunt (i.e., a current sensor), a dedicated transmitter (including a zero-sequence current transmitter, a voltage transmitter, and a current transmitter), and a signal conditioning unit.

[0153] The zero-sequence current transformer is installed on the excitation grounding wire of the synchronous motor. Its open-type structure facilitates on-site installation. Its output is connected to the zero-sequence current transmitter, linearly converting the acquired 0-10A zero-sequence current signal into a 4-20mA standard current signal. A high-precision voltage sensor (model: CSM025D, accuracy class 0.1) is used, and the acquired voltage signal is converted into a 4-20mA standard current signal by the voltage transmitter. A high-precision DC shunt (accuracy class 0.05) is used and installed on the main branch of the excitation circuit of the excitation device. In the circuit, a series connection loop collects 0-500A excitation current signals, and the voltage signals at both ends are converted into 4-20mA standard current signals by a current transmitter. The signal conditioning unit consists of a signal isolation circuit, a noise reduction filter circuit, an amplifier circuit, and a 16-bit A / D conversion chip. First, the three 4-20mA standard signals are opto-isolated by the isolation circuit to avoid the influence of strong electromagnetic interference from the excitation device. Then, the residual high-frequency noise is filtered out by a second-order Butterworth low-pass filter circuit. Finally, the analog signal is converted into a digital signal by the A / D conversion chip and transmitted to the data processing module.

[0154] The data storage module uses a combination of SD card and Flash memory. The SD card is used to store the raw electrical signal data collected by the data acquisition module and the processing result data of the data processing module, which facilitates data export and subsequent analysis. The Flash memory is used to pre-store the generator no-load characteristic curve provided in the synchronous motor's factory report. This curve is stored in the form of a data table, which facilitates the data processing module to call and compare it.

[0155] The data processing module uses TI's TMS320F28335 DSP digital signal processor, which has a fast processing speed and powerful digital signal processing capabilities. The DSP digital signal processor first receives the digital signal transmitted by the signal conditioning unit. It then uses a built-in scaling algorithm to restore the digital quantities corresponding to the 4–20mA standard signal to the actual zero-sequence current, excitation voltage, and excitation current physical quantities (restoration formula: actual value = (digital quantity corresponding to current value - 4mA) × (upper limit of range - lower limit of range) / (20mA - 4mA) + lower limit of range). Subsequently, it uses an FIR-based filtering algorithm to filter the restored physical quantity data, removing residual interference signals. Next, it uses a data normalization algorithm to convert the filtered excitation voltage and excitation current data into a numerical range that matches the generator's no-load characteristic curve. Finally, it calls the generator's no-load characteristic curve pre-stored in the Flash memory, compares the normalized excitation voltage and excitation current data point-by-point with the no-load characteristic curve, calculates the comparison deviation value, and transmits the processed electrical parameter data and the comparison deviation value to the status determination module.

[0156] The status determination module uses an STM32F103 microcontroller, which internally stores fault judgment thresholds determined through "multi-condition test calibration + statistical modeling optimization + dynamic condition correction" via EEPROM. The specific calibration process is as follows: Ten synchronous motors of the same model covering capacity levels of 100MW, 200MW, 300MW, and 600MW were selected. Different degrees of short circuit, grounding, and open circuit faults were simulated under no-load, 50% load, and rated load conditions. Each fault was tested 50 times. After outliers were removed using the outlier removal criterion of normal distribution, the lower limit of the 95% confidence interval was taken as the basic threshold. Then, the threshold system was finally determined through optimization using a four-factor regression model and adjustment of environmental and load correction coefficients: the zero-sequence current threshold is classified according to motor capacity (3A for 100MW and below, 100-300MW for 100MW and below, 3A for 100MW and below, 3A for 100MW and below, 3A for 100MW and below, and ... For 5A and 8A for 300MW and above, the excitation voltage threshold is 80% of the rated excitation voltage, the excitation current threshold is 120% of the rated excitation current, and the comparison deviation threshold is 10%. The correction coefficient can be updated through the host computer interface according to the on-site working conditions to adapt to different operating environments.

[0157] The status determination module adopts a three-level judgment mechanism of "premise verification - parameter analysis - cross-validation", and dynamically optimizes the judgment conditions in combination with the synchronous motor operating conditions. The specific judgment logic and experimental verification data are as follows:

[0158] 1) Grounding fault diagnosis:

[0159] Preliminary Verification (Steady-State Operation Confirmation): To avoid misjudgments caused by transient processes such as motor start-up and shutdown, and sudden load changes, the system first confirms whether the motor is in a stable operating state. The data processing module collects and calculates the average excitation voltage and average excitation current in real time within the last 100ms (100 sampling points, sampling frequency 1kHz). The instantaneous fluctuation is calculated using the formula "fluctuation = |(real-time value - average value) / average value| × 100%". If the excitation voltage fluctuation is ≤ ±5% of the rated value and the excitation current fluctuation is ≤ ±5% of the rated value within 20 consecutive sampling cycles (20ms), the motor is determined to be in a stable operating state, eliminating transient interference such as motor start-up and shutdown, and sudden load changes.

[0160] Basic judgment (zero-sequence current exceeds the standard): Monitor the zero-sequence current of the grounding wire. If the processed zero-sequence current data is greater than the preset zero-sequence current threshold for 3 consecutive sampling cycles (sampling frequency 1kHz, i.e., 3ms), the threshold is set according to the motor capacity classification: 3A for motors of 100MW and below, 5A for motors of 100-300MW, and 8A for motors of 300MW and above.

[0161] Cross-validation and fault decomposition:

[0162] While meeting the basic judgment requirements, it is also necessary to consider the insulation resistance of the excitation winding (indirectly calculated through the data acquisition module; the calculation formula is: insulation resistance). ,in This is the excitation voltage. It is the zero-sequence current. The reference value for leakage current in the excitation circuit (i.e.) According to the requirements of GB / T 1311-2008 "Test Methods for DC Motors", the motor was placed in a fault-free normal state, the rated excitation voltage was applied and it was maintained under no-load operation for 30 minutes. A high-precision microammeter (accuracy class 0.01) was connected in series with the excitation grounding wire to collect leakage current data in real time and record it on the factory test report. (This benchmark value is pre-stored in the data storage module) for verification. If the insulation resistance value is lower than 50MΩ and no open-circuit or short-circuit characteristic parameters are triggered, it can be determined as a grounding fault.

[0163] Grounding faults can also be classified into metallic grounding and insulation aging grounding.

[0164] Metallic grounding: If the zero-sequence current sudden change amplitude is ≥5A / ms and accompanied by a sudden drop in insulation resistance to below 50MΩ within 1s;

[0165] Insulation aging type grounding: If the zero-sequence current rises slowly at a rate of ≤0.1A / min and the insulation resistance gradually decreases at a rate of ≤5MΩ / min.

[0166] 2) Open circuit fault diagnosis:

[0167] Basic judgment (abnormal trends in electrical parameters):

[0168] The excitation current exhibits one of the following two characteristics:

[0169] 1. Sudden drop characteristics: The rate of change of the excitation current is ≥5A / ms, and the current value drops to "below 5% of the rated current corresponding to the current load", and lasts for 5 sampling cycles (5ms).

[0170] 2. Gradual descent characteristic: The excitation current changes at a rate of 0.1~5A / ms and gradually decreases below the threshold within 10s.

[0171] Excitation voltage characteristics: The excitation voltage is accompanied by a sudden drop in current, and rises to more than 90% of the power supply voltage within 10ms, and remains there for 5ms.

[0172] Cross-validation and subdivision of faults:

[0173] Rotor time constant variation: The rotor time constant τ is calculated in real time by fitting the excitation voltage step response (using the fitting formula for the excitation voltage step response). Where L is the excitation winding inductance and R is the winding resistance, both of which are factory parameters pre-stored in the data storage module. Under normal operation, τ fluctuation is ≤±15%, and under open circuit fault, the sudden increase in τ due to the disconnection of the circuit is ≥50%.

[0174] No-load characteristic curve deviation: Under the same excitation voltage, the deviation between the actual current and the standard current on the curve is ≥90%.

[0175] Interference elimination: Zero-sequence current < 50% of the corresponding capacity threshold, and no short-circuit fault characteristic parameters are triggered.

[0176] Open circuit faults can be further divided into sudden open circuits and open circuits caused by poor contact.

[0177] Sudden open circuit: meets the condition of "sudden current drop + sudden voltage rise + sudden τ change ≥ 50%";

[0178] Open circuit due to poor contact: meets the following conditions: "current gradually decreases + voltage gradually increases + τ gradually increases by ≥50% + drops to the threshold within 10 seconds".

[0179] 3) Short circuit fault diagnosis:

[0180] Prerequisite verification (excluding normal adjustment interference):

[0181] The following verification strategy is adopted according to the different operating stages of the motor:

[0182] 1. In-operation status verification (applicable to sudden short circuits while the unit is already in operation on the grid)

[0183] Excitation parameter stability: Calculate the average excitation current over the past 200ms (200 sampling points, 1kHz sampling). and average excitation voltage The formula "current fluctuation = "Voltage fluctuation = "Calculate the fluctuation amount. If the current fluctuation amount is ≤ ±8% and the voltage fluctuation amount is ≤ ±8% within 15ms, then the current excitation system is determined to be in a relatively stable state and not in a drastic adjustment process."

[0184] Preliminary no-load curve matching verification: Calculate the comparison deviation between the current excitation voltage-current data point and the stored no-load characteristic curve. If the deviation between the current voltage-current data and the no-load characteristic curve is ≤15%, it indicates that the current operating point has not seriously deviated from the normal magnetization characteristics, and any significant parameter anomalies can be ruled out.

[0185] Basic judgment:

[0186] Current characteristics: The excitation current exhibits a sudden increase (change rate ≥ 10A / ms) and remains above the excitation current threshold (120% of the rated value) for 4 consecutive sampling cycles (4ms).

[0187] Deviation characteristics: The deviation value between the voltage-current data and the no-load characteristic curve is greater than 10% for 4 consecutive sampling periods, and the deviation value shows a sudden increase (growth rate ≥ 5% / ms).

[0188] Cross-validation and fault decomposition:

[0189] Voltage characteristics: The excitation voltage is accompanied by a sudden increase in current, which drops to below 90% of the rated voltage within 10ms and lasts for 4ms.

[0190] Rotor time constant assistance: The pre-stored excitation winding resistance R parameter is called, and the rotor time constant τ=L / R is obtained by combining the real-time calculated inductance L (fitted by voltage and current response curve). During a short circuit, L decreases due to the inter-turn short circuit, and τ drops sharply by ≥30% (normal fluctuation ≤±15%).

[0191] Interference elimination: Zero-sequence current < 50% of the corresponding capacity threshold, and no open-circuit fault characteristic parameters are triggered;

[0192] 2. Initial startup status verification (applicable when a short circuit fault exists in the excitation winding before startup)

[0193] Within the initial 1 second after the excitation circuit is energized (start-up phase), if it is detected that: the excitation current directly reaches or exceeds 120% of the rated value during the establishment process, and continues for at least 4 sampling cycles (4ms); simultaneously, the excitation voltage is limited to 70% or below the rated value, which is significantly inconsistent with the normal start-up excitation establishment curve, then a short circuit fault is suspected to have existed before startup. The system will skip the basic judgment step and directly enter the cross-validation step for confirmation.

[0194] Short circuit faults can be further classified into inter-turn short circuits, multi-turn short circuits, and severe short circuits.

[0195] Inter-turn short circuit: meets the following conditions: current surge + voltage drop + τ drop ≥ 30%;

[0196] Multi-turn short circuit: The current remains high (over 150% of the rated value), the voltage remains low (≤80% of the rated value), and the deviation is stable at ≥20%;

[0197] Severe short circuit: Deviation value ≥30% and the temperature signal received by the data processing module (if equipped with a temperature sensor) shows that the excitation circuit temperature is ≥85℃ (normal ≤65℃).

[0198] In a preferred embodiment, the display module is used to display in real time the various motor parameter data collected by the data acquisition module, the processing results of the data processing module, and the fault judgment results output by the status judgment module.

[0199] Specifically, the display module consists of a TFT LCD screen and establishes a communication connection with the status determination module via SPI (Serial Peripheral Interface). This display module can dynamically refresh and display the following core information:

[0200] Real-time monitoring parameter display area: Displays raw key parameters collected and transmitted by the data acquisition module in real time, in the form of numbers or simple trend curves, including: zero-sequence current of the excitation grounding wire, excitation voltage, and excitation current. All data are displayed with clear engineering units (such as A, V), and the instantaneous value, effective value, or average value can be switched as needed.

[0201] It clearly displays the key intermediate results calculated by the data processing module, which typically include absolute deviation ( ) and relative deviation ( The results are presented numerically for easy monitoring of whether they are approaching or exceeding warning thresholds. The comprehensive diagnostic results from the status determination module are highlighted with prominent text and backlight colors (e.g., green for normal, red for fault). These results are presented directly in Chinese status terms, such as "Normal," "Short Circuit Fault," "Ground Fault," and "Open Circuit Fault." When a fault is diagnosed, this area can simultaneously display the confidence level or severity level of the fault.

[0202] In a preferred embodiment, the alarm module is used to issue an audible and visual alarm signal when the status determination module determines that there is a short circuit, grounding, or open circuit abnormality.

[0203] Specifically, the visual alarm unit uses a red LED indicator. When an abnormal status signal is received from the status determination module, the red LED indicator immediately enters a rapid flashing mode (e.g., a flashing frequency of 2 Hz) to indicate that the device is in an abnormal state.

[0204] Auditory alarm unit: Employs a buzzer. Synchronized with the visual alarm unit, the buzzer emits intermittent "beep" alarm sounds; the alarm sounds once per second, ensuring it can be detected even in noisy industrial environments.

[0205] The alarm module is immediately activated when the status determination module outputs a short circuit, grounding, or open circuit abnormality judgment result. The red LED begins to flash, and the buzzer sounds simultaneously. The alarm signal will continue to be emitted without automatic cessation to prevent alarm information from being missed. Once on-site personnel are aware of the alarm and arrive at the scene, they must manually press the reset button on the equipment panel to deactivate the audible and visual alarm signal.

[0206] Example 3

[0207] According to embodiments of the present invention, the present invention also provides an electronic device, a readable storage medium, and a computer program product.

[0208] Figure 3 A schematic block diagram of an example electronic device 100 that can be used to implement embodiments of the present invention is shown. Electronic device 100 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic device 100 may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their links and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0209] like Figure 3As shown, the electronic device 100 includes a computing unit 101, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 102 or a computer program loaded from a storage unit 108 into a random access memory (RAM) 103. The RAM 103 may also store various programs and data required for the operation of the electronic device 100. The computing unit 101, ROM 102, and RAM 103 are interconnected via a bus 104. An input / output (I / O) interface 105 is also linked to the bus 104.

[0210] Multiple components in electronic device 100 are linked to I / O interface 105, including: input unit 106, such as keyboard, mouse, etc.; output unit 107, such as various types of displays, speakers, etc.; storage unit 108, such as disk, optical disk, etc.; and communication unit 109, such as network card, modem, wireless transceiver, etc. Communication unit 109 allows electronic device 100 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0211] The computing unit 101 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 101 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 101 performs the various methods and processes described above, such as a method for detecting abnormal states of a synchronous motor excitation winding. For example, in some embodiments, a method for detecting abnormal states of a synchronous motor excitation winding can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 108. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 100 via ROM 102 and / or communication unit 109. When the computer program is loaded into RAM 103 and executed by the computing unit 101, one or more steps of the method for detecting abnormal states of a synchronous motor excitation winding described above can be performed. Alternatively, in other embodiments, the computing unit 101 may be configured by any other suitable means (e.g., by means of firmware) to perform a method for detecting abnormal states of synchronous motor excitation windings.

[0212] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0213] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0214] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical links based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0215] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0216] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0217] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0218] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this invention can be achieved, and this is not limited herein.

[0219] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Therefore, any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for detecting abnormal conditions in the excitation winding of a synchronous motor, characterized in that, Includes the following steps: Acquire the zero-sequence current data of the grounding wire of the excitation winding, the excitation voltage data, and the excitation current data; Store the no-load characteristic curve data of the synchronous motor, as well as the inductance and resistance parameters of the excitation winding; The grounding wire zero-sequence current data, excitation voltage data, and excitation current data are preprocessed to obtain normalized data. The excitation voltage data and excitation current data in the normalized data are then compared with the no-load characteristic curve data to generate corresponding comparison deviation data. Based on the standardized data and the comparison deviation data, combined with the preset state judgment rules, it is determined whether the excitation winding is in a short circuit, open circuit or grounding abnormal state, and the corresponding state judgment result is output. The preset state determination rules include the following logical determination process: Grounding status determination logic: Under the premise that the synchronous motor is determined to be in a stable operating state, if the grounding wire zero-sequence current data continuously exceeds the grounding wire zero-sequence current threshold, and the insulation resistance data of the excitation winding is lower than the preset insulation resistance threshold, then the excitation winding is determined to be in an abnormal grounding state. Open circuit state determination logic: If the excitation current data shows a downward trend and the excitation voltage data shows an upward trend, and the rotor time constant of the excitation winding changes more than the preset time constant change threshold, then the excitation winding is determined to be in an open circuit abnormal state. Short circuit condition determination logic: If the excitation current data shows an upward trend, the excitation voltage data shows a downward trend, and the comparison deviation data exceeds the comparison deviation threshold, and the rotor time constant change exceeds the preset time constant change threshold, then the excitation winding is determined to be in a short circuit abnormal state.

2. The method for detecting abnormal conditions of the excitation winding of a synchronous motor according to claim 1, characterized in that, The storage of the no-load characteristic curve data of the synchronous motor, as well as the inductance and resistance parameters of the excitation winding, specifically includes the following steps: Receive and parse structured data files from motor factory reports or remote databases. The structured data files contain at least no-load characteristic curves expressed in the form of voltage-current data pairs, as well as the rated inductance and rated resistance values ​​of the excitation winding. The voltage-current data sequence of the no-load characteristic curve is normalized and interpolated to generate a standardized no-load characteristic curve lookup table. The standardized no-load characteristic curve lookup table is then associated with the rated inductance value and rated resistance value and stored as a characteristic parameter set of the synchronous motor. Based on the historical operating data or online identification results of the synchronous motor, the estimated values ​​of the inductance and resistance parameters are stored in the characteristic parameter set.

3. The method for detecting abnormal excitation winding status of a synchronous motor according to claim 1, characterized in that, The preprocessing of the grounding wire zero-sequence current data, excitation voltage data, and excitation current data to obtain normalized data, and the comparison of the normalized excitation voltage data and excitation current data with the no-load characteristic curve data to generate corresponding comparison deviation data, specifically includes the following steps: The acquired grounding wire zero-sequence current data, excitation voltage data, and excitation current data are digitally filtered. The excitation voltage data and excitation current data are normalized, wherein the normalization process maps the actual data to a preset numerical range using the following formula: ； in, , These are the actual excitation voltage and excitation current values, respectively. , These are the preset upper and lower limits of the excitation voltage, respectively. , These are the preset upper and lower limits of the excitation current, respectively; The normalized excitation voltage data and excitation current data are compared with the no-load characteristic curve data to calculate the comparison deviation data.

4. The method for detecting abnormal conditions of the excitation winding of a synchronous motor according to claim 2, characterized in that, The step of comparing the normalized excitation voltage data and excitation current data with the no-load characteristic curve data to calculate the comparison deviation data specifically includes the following steps: Based on the normalized excitation current value The corresponding expected value of the standard excitation voltage is obtained by interpolation lookup in the standardized no-load characteristic curve lookup table. ; Calculate the measured normalized excitation voltage value Compared with the expected value of the standard excitation voltage absolute deviation and relative deviation The calculation formulas are as follows: ; The absolute deviation With the relative deviation Together they serve as the comparison deviation data.

5. The method for detecting abnormal conditions of the excitation winding of a synchronous motor according to claim 1, characterized in that, The acquisition of the zero-sequence current data of the grounding wire of the excitation winding, the excitation voltage data, and the excitation current data specifically includes the following steps: Establish a data communication connection with an external monitoring system, which includes at least a zero-sequence current transformer located on the grounding wire of the excitation winding, a voltage sensor located at both ends of the excitation winding, and a current sensor located in the excitation circuit. According to the preset sampling frequency and data format, the raw data streams of the grounding wire zero-sequence current data, excitation voltage data and excitation current data uploaded by the external monitoring system are synchronously received through the data communication connection. The original data stream is timestamped and its data packet integrity is verified. Abnormal timestamp data and incomplete data packets are removed to form time-series data.

6. The method for detecting abnormal conditions of the excitation winding of a synchronous motor according to claim 1, characterized in that, It also includes the following steps: Based on the type and severity level of the status determination result, a diagnostic report is generated that includes an anomaly code, suggested handling measures, and estimated risk level. The diagnostic report, the key data sequence that triggered the judgment, and the corresponding comparison deviation data are packaged and stored in the historical fault case library; If the determination result is a short circuit or grounding abnormality, an early warning command for reduced load operation or shutdown inspection will be automatically generated and sent to the synchronous motor monitoring system.

7. A detection device for abnormal excitation winding condition of a synchronous motor, characterized in that, include: The data acquisition module is used to acquire the zero-sequence current data of the grounding wire of the excitation winding, the excitation voltage data, and the excitation current data. The data storage module is used to store the no-load characteristic curve data of the synchronous motor, as well as the inductance and resistance parameters of the excitation winding. The data processing module is used to preprocess the grounding wire zero-sequence current data, excitation voltage data, and excitation current data to obtain normalized data, and compare the excitation voltage data and excitation current data in the normalized data with the no-load characteristic curve data to generate corresponding comparison deviation data. The status determination module is used to determine whether the excitation winding is in a short circuit, open circuit or grounding abnormal state based on the normalized data and the comparison deviation data, combined with the preset status determination rules, and output the corresponding status determination result. The preset state determination rules include the following logical determination process: Grounding status determination logic: Under the premise that the synchronous motor is determined to be in a stable operating state, if the grounding wire zero-sequence current data continuously exceeds the grounding wire zero-sequence current threshold, and the insulation resistance data of the excitation winding is lower than the preset insulation resistance threshold, then the excitation winding is determined to be in an abnormal grounding state. Open circuit state determination logic: If the excitation current data shows a downward trend and the excitation voltage data shows an upward trend, and the rotor time constant of the excitation winding changes more than the preset time constant change threshold, then the excitation winding is determined to be in an open circuit abnormal state. Short circuit condition determination logic: If the excitation current data shows an upward trend, the excitation voltage data shows a downward trend, and the comparison deviation data exceeds the comparison deviation threshold, and the rotor time constant change exceeds the preset time constant change threshold, then the excitation winding is determined to be in a short circuit abnormal state.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the detection method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the detection method according to any one of claims 1-6.

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